[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M74F6CSNN1J9FXCSJC8QJ6PY":3,"skill-same:01M74F6CSNN1J9FXCSJC8QJ6PY":107},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":15,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":13,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":36,"install":37,"resources":50},"01M74F6CSNN1J9FXCSJC8QJ6PY","mem0",null,"Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions \"mem0\", \"MemoryClient\", \"memory layer\", \"remember user preferences\", \"persistent context\", \"personalization\", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK \u002F @mem0\u002Fvercel-ai-provider \u002F createMem0 (use mem0-vercel-ai-sdk).","lzy18001500226\u002FMoSim","lzy18001500226","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F152623587?v=4","01M71ZMEX36NK05B642131DEEK","01M71ZMEXSMX72KPVNJ572G0CM",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: mem0\ndescription: >\n  Mem0 Platform SDK for adding persistent memory to AI applications.\n  TRIGGER when: user mentions \"mem0\", \"MemoryClient\", \"memory layer\",\n  \"remember user preferences\", \"persistent context\", \"personalization\",\n  or needs to add long-term memory to chatbots, agents, or AI apps.\n  Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations\n  (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph).\n  Also covers the open-source self-hosted Memory class.\n  This is the DEFAULT mem0 skill for ambiguous queries.\n  DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell\n  scripts (use mem0-cli), or Vercel AI SDK \u002F @mem0\u002Fvercel-ai-provider \u002F createMem0\n  (use mem0-vercel-ai-sdk).\nlicense: Apache-2.0\nmetadata:\n  author: mem0ai\n  version: \"3.0.0\"\n  category: ai-memory\n  tags: \"memory, personalization, ai, python, typescript, vector-search\"\ncompatibility: Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. SDK v3 with v2 compatibility mode available.\n---\n\n# Mem0 Platform Integration\n\n> **Skill Graph:** This skill is part of the Mem0 skill graph:\n> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)\n> - **[mem0-cli](..\u002Fmem0-cli\u002FSKILL.md)** ([GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-cli)) -- Command-line interface\n> - **[mem0-vercel-ai-sdk](..\u002Fmem0-vercel-ai-sdk\u002FSKILL.md)** ([GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-vercel-ai-sdk)) -- Vercel AI SDK provider\n\nMem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.\n\n## Step 1: Install and authenticate\n\n**Python:**\n```bash\npip install mem0ai\nexport MEM0_API_KEY=\"m0-your-api-key\"\n```\n\n**TypeScript\u002FJavaScript:**\n```bash\nnpm install mem0ai\nexport MEM0_API_KEY=\"m0-your-api-key\"\n```\n\nGet an API key at: https:\u002F\u002Fapp.mem0.ai\u002Fdashboard\u002Fapi-keys?utm_source=oss&utm_medium=skill-mem0\n\n> **Don't have a `MEM0_API_KEY`?** Run `mem0 init --agent --agent-caller \u003Cyour-name> --json` (after `pip install mem0-cli` or `npm install -g @mem0\u002Fcli`), substituting your agent identity (e.g. `claude-code`, `cursor`). If you forgot to pass `--agent-caller`, run `mem0 identify \u003Cyour-name>` after init. The human can claim later with `mem0 init --email \u003Cyour-email>`.\n\n## Step 2: Initialize the client\n\n**Python:**\n```python\nfrom mem0 import MemoryClient\nclient = MemoryClient(api_key=\"m0-xxx\")\n```\n\n**TypeScript:**\n```typescript\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: 'm0-xxx' });\n```\n\nFor async Python, use `AsyncMemoryClient`.\n\n## Step 3: Core operations\n\nEvery Mem0 integration follows the same pattern: **retrieve → generate → store**.\n\n### Add memories\n```python\nmessages = [\n    {\"role\": \"user\", \"content\": \"I'm a vegetarian and allergic to nuts.\"},\n    {\"role\": \"assistant\", \"content\": \"Got it! I'll remember that.\"}\n]\nclient.add(messages, user_id=\"alice\")\n```\n\n### Search memories\n```python\nresults = client.search(\"dietary preferences\", filters={\"user_id\": \"alice\"})\nfor mem in results.get(\"results\", []):\n    print(mem[\"memory\"])\n```\n\n### Get all memories\n```python\nall_memories = client.get_all(filters={\"user_id\": \"alice\"})\n```\n\n### Update a memory\n```python\nclient.update(\"memory-uuid\", text=\"Updated: vegetarian, nut allergy, prefers organic\")\n```\n\n### Delete a memory\n```python\nclient.delete(\"memory-uuid\")\nclient.delete_all(user_id=\"alice\")  # delete all for a user\n```\n\n## Common integration pattern\n\n```python\nfrom mem0 import MemoryClient\nfrom openai import OpenAI\n\nmem0 = MemoryClient()\nopenai = OpenAI()\n\ndef chat(user_input: str, user_id: str) -> str:\n    # 1. Retrieve relevant memories\n    memories = mem0.search(user_input, filters={\"user_id\": user_id})\n    context = \"\\n\".join([m[\"memory\"] for m in memories.get(\"results\", [])])\n\n    # 2. Generate response with memory context\n    response = openai.chat.completions.create(\n        model=\"gpt-5-mini\",\n        messages=[\n            {\"role\": \"system\", \"content\": f\"User context:\\n{context}\"},\n            {\"role\": \"user\", \"content\": user_input},\n        ]\n    )\n    reply = response.choices[0].message.content\n\n    # 3. Store interaction for future context\n    mem0.add(\n        [{\"role\": \"user\", \"content\": user_input}, {\"role\": \"assistant\", \"content\": reply}],\n        user_id=user_id\n    )\n    return reply\n```\n\n## Common edge cases\n\n- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive) and use `filters={\"user_id\": \"...\"}` syntax.\n- **AND filter with user_id + agent_id returns empty:** Entities are stored separately. Use `OR` instead, or query separately.\n- **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. Stick to one mode.\n- **Wrong import:** Always use `from mem0 import MemoryClient` (or `AsyncMemoryClient` for async). Do not use `from mem0 import Memory`.\n- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed for your use case.\n\n## v2 Compatibility\n\nIf you're using SDK v2.x, note these differences:\n- **Entity IDs:** Pass `user_id` as top-level kwarg to `search()` instead of inside `filters`\n- **Defaults:** `top_k=100`, no threshold, `rerank=True`\n- **Graph memory:** Available via `enable_graph=True`\n\nSee the [migration guide](https:\u002F\u002Fdocs.mem0.ai\u002Fmigration\u002Foss-v2-to-v3) for details.\n\n## Live documentation search\n\nFor the latest docs beyond what's in the references, use the doc search tool:\n\n```bash\npython ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fmem0_doc_search.py --query \"topic\"\npython ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fmem0_doc_search.py --page \"\u002Fplatform\u002Ffeatures\u002Fgraph-memory\"\npython ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fmem0_doc_search.py --index\n```\n\nNo API key needed — searches docs.mem0.ai directly.\n\n## Client SDK References\n\nLanguage-specific deep references (Platform + OSS):\n\n| Language | File |\n|----------|------|\n| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client\u002Fpython.md](client\u002Fpython.md) |\n| TypeScript\u002FNode.js (MemoryClient + Memory OSS) | [client\u002Fnode.md](client\u002Fnode.md) |\n| Python vs TypeScript differences | [client\u002Fdifferences.md](client\u002Fdifferences.md) |\n\n## Platform References\n\nLoad these on demand for deeper detail:\n\n| Topic | File |\n|-------|------|\n| Quickstart (Python, TS, cURL) | [references\u002Fquickstart.md](references\u002Fquickstart.md) |\n| SDK guide (all methods, both languages) | [references\u002Fsdk-guide.md](references\u002Fsdk-guide.md) |\n| API reference (endpoints, filters, object schema) | [references\u002Fapi-reference.md](references\u002Fapi-reference.md) |\n| Architecture (pipeline, lifecycle, scoping, performance) | [references\u002Farchitecture.md](references\u002Farchitecture.md) |\n| Platform features (retrieval, graph, categories, MCP, etc.) | [references\u002Ffeatures.md](references\u002Ffeatures.md) |\n| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references\u002Fintegration-patterns.md](references\u002Fintegration-patterns.md) |\n| Use cases & examples (real-world patterns with code) | [references\u002Fuse-cases.md](references\u002Fuse-cases.md) |\n\n## Related Mem0 Skills\n\n| Skill | When to use | Link |\n|-------|-------------|------|\n| mem0-cli | Terminal commands, scripting, CI\u002FCD, agent tool loops | [local](..\u002Fmem0-cli\u002FSKILL.md) \u002F [GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-cli) |\n| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [local](..\u002Fmem0-vercel-ai-sdk\u002FSKILL.md) \u002F [GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-vercel-ai-sdk) |\n","# Mem0 Platform Integration\n\n> **Skill Graph:** This skill is part of the Mem0 skill graph:\n> - **mem0** (this skill) -- Platform Client SDK + OSS (Python + TypeScript)\n> - **[mem0-cli](..\u002Fmem0-cli\u002FSKILL.md)** ([GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-cli)) -- Command-line interface\n> - **[mem0-vercel-ai-sdk](..\u002Fmem0-vercel-ai-sdk\u002FSKILL.md)** ([GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-vercel-ai-sdk)) -- Vercel AI SDK provider\n\nMem0 is a managed memory layer for AI applications. It stores, retrieves, and manages user memories via API — no infrastructure to deploy. For self-hosted usage, see the OSS section in the client references below.\n\n## Step 1: Install and authenticate\n\n**Python:**\n```bash\npip install mem0ai\nexport MEM0_API_KEY=\"m0-your-api-key\"\n```\n\n**TypeScript\u002FJavaScript:**\n```bash\nnpm install mem0ai\nexport MEM0_API_KEY=\"m0-your-api-key\"\n```\n\nGet an API key at: https:\u002F\u002Fapp.mem0.ai\u002Fdashboard\u002Fapi-keys?utm_source=oss&utm_medium=skill-mem0\n\n> **Don't have a `MEM0_API_KEY`?** Run `mem0 init --agent --agent-caller \u003Cyour-name> --json` (after `pip install mem0-cli` or `npm install -g @mem0\u002Fcli`), substituting your agent identity (e.g. `claude-code`, `cursor`). If you forgot to pass `--agent-caller`, run `mem0 identify \u003Cyour-name>` after init. The human can claim later with `mem0 init --email \u003Cyour-email>`.\n\n## Step 2: Initialize the client\n\n**Python:**\n```python\nfrom mem0 import MemoryClient\nclient = MemoryClient(api_key=\"m0-xxx\")\n```\n\n**TypeScript:**\n```typescript\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: 'm0-xxx' });\n```\n\nFor async Python, use `AsyncMemoryClient`.\n\n## Step 3: Core operations\n\nEvery Mem0 integration follows the same pattern: **retrieve → generate → store**.\n\n### Add memories\n```python\nmessages = [\n    {\"role\": \"user\", \"content\": \"I'm a vegetarian and allergic to nuts.\"},\n    {\"role\": \"assistant\", \"content\": \"Got it! I'll remember that.\"}\n]\nclient.add(messages, user_id=\"alice\")\n```\n\n### Search memories\n```python\nresults = client.search(\"dietary preferences\", filters={\"user_id\": \"alice\"})\nfor mem in results.get(\"results\", []):\n    print(mem[\"memory\"])\n```\n\n### Get all memories\n```python\nall_memories = client.get_all(filters={\"user_id\": \"alice\"})\n```\n\n### Update a memory\n```python\nclient.update(\"memory-uuid\", text=\"Updated: vegetarian, nut allergy, prefers organic\")\n```\n\n### Delete a memory\n```python\nclient.delete(\"memory-uuid\")\nclient.delete_all(user_id=\"alice\")  # delete all for a user\n```\n\n## Common integration pattern\n\n```python\nfrom mem0 import MemoryClient\nfrom openai import OpenAI\n\nmem0 = MemoryClient()\nopenai = OpenAI()\n\ndef chat(user_input: str, user_id: str) -> str:\n    # 1. Retrieve relevant memories\n    memories = mem0.search(user_input, filters={\"user_id\": user_id})\n    context = \"\\n\".join([m[\"memory\"] for m in memories.get(\"results\", [])])\n\n    # 2. Generate response with memory context\n    response = openai.chat.completions.create(\n        model=\"gpt-5-mini\",\n        messages=[\n            {\"role\": \"system\", \"content\": f\"User context:\\n{context}\"},\n            {\"role\": \"user\", \"content\": user_input},\n        ]\n    )\n    reply = response.choices[0].message.content\n\n    # 3. Store interaction for future context\n    mem0.add(\n        [{\"role\": \"user\", \"content\": user_input}, {\"role\": \"assistant\", \"content\": reply}],\n        user_id=user_id\n    )\n    return reply\n```\n\n## Common edge cases\n\n- **Search returns empty:** Memories process asynchronously. Wait 2-3s after `add()` before searching. Also verify `user_id` matches exactly (case-sensitive) and use `filters={\"user_id\": \"...\"}` syntax.\n- **AND filter with user_id + agent_id returns empty:** Entities are stored separately. Use `OR` instead, or query separately.\n- **Duplicate memories:** Don't mix `infer=True` (default) and `infer=False` for the same data. Stick to one mode.\n- **Wrong import:** Always use `from mem0 import MemoryClient` (or `AsyncMemoryClient` for async). Do not use `from mem0 import Memory`.\n- **v3 defaults:** `top_k=20`, `threshold=0.1`, `rerank=False`. Adjust as needed for your use case.\n\n## v2 Compatibility\n\nIf you're using SDK v2.x, note these differences:\n- **Entity IDs:** Pass `user_id` as top-level kwarg to `search()` instead of inside `filters`\n- **Defaults:** `top_k=100`, no threshold, `rerank=True`\n- **Graph memory:** Available via `enable_graph=True`\n\nSee the [migration guide](https:\u002F\u002Fdocs.mem0.ai\u002Fmigration\u002Foss-v2-to-v3) for details.\n\n## Live documentation search\n\nFor the latest docs beyond what's in the references, use the doc search tool:\n\n```bash\npython ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fmem0_doc_search.py --query \"topic\"\npython ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fmem0_doc_search.py --page \"\u002Fplatform\u002Ffeatures\u002Fgraph-memory\"\npython ${CLAUDE_SKILL_DIR}\u002Fscripts\u002Fmem0_doc_search.py --index\n```\n\nNo API key needed — searches docs.mem0.ai directly.\n\n## Client SDK References\n\nLanguage-specific deep references (Platform + OSS):\n\n| Language | File |\n|----------|------|\n| Python (MemoryClient + AsyncMemoryClient + Memory OSS) | [client\u002Fpython.md](client\u002Fpython.md) |\n| TypeScript\u002FNode.js (MemoryClient + Memory OSS) | [client\u002Fnode.md](client\u002Fnode.md) |\n| Python vs TypeScript differences | [client\u002Fdifferences.md](client\u002Fdifferences.md) |\n\n## Platform References\n\nLoad these on demand for deeper detail:\n\n| Topic | File |\n|-------|------|\n| Quickstart (Python, TS, cURL) | [references\u002Fquickstart.md](references\u002Fquickstart.md) |\n| SDK guide (all methods, both languages) | [references\u002Fsdk-guide.md](references\u002Fsdk-guide.md) |\n| API reference (endpoints, filters, object schema) | [references\u002Fapi-reference.md](references\u002Fapi-reference.md) |\n| Architecture (pipeline, lifecycle, scoping, performance) | [references\u002Farchitecture.md](references\u002Farchitecture.md) |\n| Platform features (retrieval, graph, categories, MCP, etc.) | [references\u002Ffeatures.md](references\u002Ffeatures.md) |\n| Framework integrations (LangChain, CrewAI, OpenAI Agents, etc.) | [references\u002Fintegration-patterns.md](references\u002Fintegration-patterns.md) |\n| Use cases & examples (real-world patterns with code) | [references\u002Fuse-cases.md](references\u002Fuse-cases.md) |\n\n## Related Mem0 Skills\n\n| Skill | When to use | Link |\n|-------|-------------|------|\n| mem0-cli | Terminal commands, scripting, CI\u002FCD, agent tool loops | [local](..\u002Fmem0-cli\u002FSKILL.md) \u002F [GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-cli) |\n| mem0-vercel-ai-sdk | Vercel AI SDK provider with automatic memory | [local](..\u002Fmem0-vercel-ai-sdk\u002FSKILL.md) \u002F [GitHub](https:\u002F\u002Fgithub.com\u002Fmem0ai\u002Fmem0\u002Ftree\u002Fmain\u002Fskills\u002Fmem0-vercel-ai-sdk) |\n",{"name":5,"license":14,"metadata":24,"description":29,"compatibility":30},{"tags":25,"author":26,"version":27,"category":28},"memory, personalization, ai, python, typescript, vector-search","mem0ai","3.0.0","ai-memory","Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions \"mem0\", \"MemoryClient\", \"memory layer\", \"remember user preferences\", \"persistent context\", \"personalization\", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK (mem0ai), and framework integrations (LangChain, CrewAI, OpenAI Agents SDK, Pipecat, LlamaIndex, AutoGen, LangGraph). Also covers the open-source self-hosted Memory class. This is the DEFAULT mem0 skill for ambiguous queries. DO NOT TRIGGER when: user asks about CLI commands, terminal usage, or shell scripts (use mem0-cli), or Vercel AI SDK \u002F @mem0\u002Fvercel-ai-provider \u002F createMem0 (use mem0-vercel-ai-sdk).\n","Requires Python 3.10+ or Node.js 18+, pip install mem0ai or npm install mem0ai, MEM0_API_KEY env var (Platform), and internet access to api.mem0.ai. SDK v3 with v2 compatibility mode available.","main","e0ec177d00f087cb04e90206b2f7d3aa2f5a1350","References\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim","https:\u002F\u002Fgithub.com\u002Flzy18001500226","2026-08-31 23:29:00",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx skills add lzy18001500226\u002FMoSim --skill mem0",{"tool":43,"command":44},"bunx","bunx skills add lzy18001500226\u002FMoSim --skill mem0",{"tool":46,"command":47},"pnpm","pnpm dlx skills add lzy18001500226\u002FMoSim --skill mem0","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002FSKILL.md",true,[51,57,61,65,69,73,77,81,85,89,93,97,101],{"path":52,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":55,"sourceHtmlUrl":56},"client\u002Fdifferences.md","REFERENCE","参考文档",4978,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Fclient\u002Fdifferences.md",{"path":58,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":59,"sourceHtmlUrl":60},"client\u002Fnode.md",11540,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Fclient\u002Fnode.md",{"path":62,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":63,"sourceHtmlUrl":64},"client\u002Fpython.md",12858,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Fclient\u002Fpython.md",{"path":66,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":15},"LICENSE","许可证",10705,{"path":70,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":71,"sourceHtmlUrl":72},"README.md",3452,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002FREADME.md",{"path":74,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":75,"sourceHtmlUrl":76},"references\u002Fapi-reference.md",4616,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Fapi-reference.md",{"path":78,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Farchitecture.md",11227,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Farchitecture.md",{"path":82,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Ffeatures.md",11357,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Ffeatures.md",{"path":86,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fintegration-patterns.md",11692,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Fintegration-patterns.md",{"path":90,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fquickstart.md",2919,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Fquickstart.md",{"path":94,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fsdk-guide.md",9382,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Fsdk-guide.md",{"path":98,"resourceType":53,"resourceTypeDesc":54,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fuse-cases.md",22540,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Freferences\u002Fuse-cases.md",{"path":102,"resourceType":103,"resourceTypeDesc":104,"sizeBytes":105,"sourceHtmlUrl":106},"scripts\u002Fmem0_doc_search.py","SCRIPT","脚本",8154,"https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FMemory\u002Fmem0\u002Fskills\u002Fmem0\u002Fscripts\u002Fmem0_doc_search.py",[108,117,125,133,141,149],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":112,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":116},"01M78RK6JGRPTD3TDSMTQWDQ9P","skill-installer","Install Codex skills into $CODEX_HOME\u002Fskills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos).","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fskill-installer\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fskill-installer\u002FSKILL.md",[],[],"2026-11-15 14:19:17",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":121,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":116},"01M78RK29KVRDQXSMVZX1GQTJN","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fskill-creator\u002Flicense.txt","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fskill-creator\u002FSKILL.md",[],[],{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":129,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":116},"01M78RJPF3210Y5DA9VJ6JMMG6","openai-docs","Use for Codex models\u002Fpricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs\u002Fproducts and ChatGPT Work. Also use for model choice\u002Fmigration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat\u002FWork\u002FCodex comparisons. Do not use for generic app\u002Fsoftware tasks that merely mention Codex.","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fopenai-docs\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fopenai-docs\u002FSKILL.md",[],[],{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":137,"sourceHtmlUrl":138,"categories":139,"keywords":140,"publishedAt":116},"01M78RJH7X67C5AY8ZVS7RQNWJ","imagegen","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fimagegen\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002Fsrc\u002FAgent\u002Fcodex-main\u002Fcodex-rs\u002Fskills\u002Fsrc\u002Fassets\u002Fsamples\u002Fimagegen\u002FSKILL.md",[],[],{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":145,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":116},"01M78RBBAYMJ6T3ZMAYHCFM6G7","skill-creator-cc","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FWorkflow\u002Fokwinds\u002Fmiscellany\u002Fagent\u002Fskills\u002Fskill-creator-cc\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FWorkflow\u002Fokwinds\u002Fmiscellany\u002Fagent\u002Fskills\u002Fskill-creator-cc\u002FSKILL.md",[],[],{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":153,"licenseFileUrl":154,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":158},"01M76VV9V2R9FZTVGMJZV7Y996","prd-writing-guide","Write complete, unambiguous PRDs that development teams can implement without guesswork. Includes requirement discovery framework, structured documentation methodology, completeness checklists, and common pitfall avoidance. Use when: writing new PRDs, reviewing PRD drafts, validating requirement completeness, preparing for engineering handoff. Triggers: 'write PRD', '写PRD', '产品需求文档', '需求文档', '需求规格', '需求评审', '完善需求', 'create requirements doc', 'product requirements', 'feature spec', 'requirements document'. Anti-triggers: 'technical design doc', 'architecture design', 'implementation plan', 'API design', '架构设计', '技术方案', '实现方案', '接口设计'.","MIT","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FWorkflow\u002Fokwinds\u002Fmiscellany\u002Fagent\u002Fskills\u002Fprd-writing-guide\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Flzy18001500226\u002FMoSim\u002Fblob\u002Fmain\u002FReferences\u002FAgent\u002FWorkflow\u002Fokwinds\u002Fmiscellany\u002Fagent\u002Fskills\u002Fprd-writing-guide\u002FSKILL.md",[],[],"2026-11-12 13:55:43"]